Breaking Up Is Not That Hard To Do

Based on research bySharad Borle, Siddharth S. Singh and Dipak C. Jain

Contrary to what one might think, customers who stay with a firm longer may not spend more money than those who stay for less time.

Determining a customer‚Äôs spending in a buying relationship will shape how managers commit marketing dollars.

Longer spells between purchases are linked with greater risk of a customer leaving a firm.

Managers dream of keeping their customers faithful for years. Sooner or later, though, almost all consumers will go elsewhere. That‚Äôs why marketing experts scramble to predict how long a customer will stay with a company and how much money she‚Äôll spend on it. In industry parlance, this variable is ‚Äúcustomer lifetime value.‚ÄĚ

The craving to know why customers stray has inspired a cottage industry in models calculating the ‚Äúlifetime value‚ÄĚ a customer brings to his or her partnership with a seller. Pinpointing when a customer starts doing business with a firm, each purchase she makes and when she quits buying all help marketers hone their efforts to keep her interested.

But customer devotion rarely runs smoothly. Higher spending, for one thing, does not necessarily correlate with longer customer relationship. Curiously, consumers who stay with a company briefly often spend more than those who stay loyal for years. So managers need to differentiate between the length of time a customer buys from them and her ‚Äúlifetime value‚ÄĚ ‚Äď what she brings to that connection.

To improve companies‚Äô predictive ability, Rice Business professors Sharad Borle and Siddharth S. Singh, along with colleague Dipak C. Jain, then of Northwestern University, created a model for predicting and testing customer lifetime value. They define this as the value a customer brings to a firm (generally measured as the revenues from her purchases), minus the firm‚Äôs costs to maintain the bond.

Past research on this topic has focused on two specific contexts: contractual and non-contractual. In a non-contractual situation, management doesn‚Äôt know exactly when customers quit buying a product, so the moment of a customer‚Äôs defection has to be inferred. Contractual situations, on the other hand, allow close monitoring of specific day-to-day factors. In contractual relationships such as automobile associations, membership-based retailers like Costco and membership-based buying relationships such as music and food clubs, managers know exactly when a relationship starts and ends, and every time a customer makes a purchase.

The researchers decided to look at elements of both contractual and non-contractual settings in their study, a scenario that had not been previously analyzed in any depth. First they looked at a membership-based direct marketing company that tracked the dates each customer joined and terminated membership. Then they examined how well different models worked in predicting customer lifetime value.

Their model predicted customers‚Äô likelihood of ending the membership as well as their spending patterns. With this information in hand, the scholars could estimate the lifetime value of each customer every time she made a purchase.

The model proved better at predicting customer lifetime value and in targeting valuable customers than the other models to which they compared it. They also discovered that a customer‚Äôs purchase timing, purchase amount and risk of defecting are intertwined, which validated their joint-modeling approach.

To reach their conclusions, the researchers studied two random samples of data, both drawn from the population of all customers who joined a company in one particular year in the late 1990s. These data offered information about all purchases by these customers from the first day of membership to the time the consumers moved on to other pastures.

The Ô¨Ārst part of the data showed 1,000 past customers and 7,108 purchases. It traced the buying habits of these customers over their entire lifetime with the Ô¨Ārm. The second part, consisting of another 500 past customers (a validation sample), was selected for predictive testing and to illustrate the model in action. It looked at the times between purchase, purchase amounts and the total membership lifetime of each customer.

How did loyalty correlate to spending? Not very strongly. A customer who waited longer between purchases was more likely to end her membership, yet a customer who waited longer between purchases also was likely to spend more. There was little difference between the amount of time men and women waited to buy ‚Äď but when they did make a purchase, women spent less. Men and women were equally likely to end their membership with the firm.

Gauging customer value, clearly, is no simple thing. But good models that predict how and when customers spend remove some of the guesswork. It‚Äôs crucial data for any business that needs to know when customers are thinking of bolting ‚Äď and how to woo them back.

Sharad Borle is an associate professor of marketing at Jones Graduate School of Business at Rice University. Siddharth S. Singh, formerly at Rice Business, is now a marketing professor at the Indian School of Business.